Preoperative Nomogram for Differentiation of Histological Subtypes in Ovarian Cancer Based on Computer Tomography Radiomics

Frontiers in Oncology

Xiance Jin²  Jindi Zhang²  Haiyan Zhu²  Juebin Jin³  Huafang Su⁴  Congying Xie⁵  Yao Ai⁶  Ji Zhang⁶ 

[1] Department of Gynecology, Shanghai First Maternal and Infant Hospital, Tongji University School of Medicine, Shanghai, China;Department of Gynecology, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China;Department of Medical Engineering, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China;Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China;Department of Radiation and Medical Oncology, The 2nd Affiliated Hospital of Wenzhou Medical University, Wenzhou, China;Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China;

关键词: ovarian cancer;    epithelial ovarian cancer;    non-epithelial ovarian cancer;    computed tomography;    radiomics;    nomogram;   

DOI  :  10.3389/fonc.2021.642892

来源: DOAJ

【 摘 要 】

ObjectivesNon-invasive method to predict the histological subtypes preoperatively is essential for the overall management of ovarian cancer (OC). The feasibility of radiomics in the differentiating of epithelial ovarian cancer (EOC) and non-epithelial ovarian cancer (NEOC) based on computed tomography (CT) images was investigated.MethodsRadiomics features were extracted from preoperative CT for 101 patients with pathologically proven OC. Radiomics signature was built using the least absolute shrinkage and selection operator (LASSO) logistic regression. A nomogram was developed with the combination of radiomics features and clinical factors to differentiate EOC and NEOC.ResultsEight radiomics features were selected to build a radiomics signature with an area under curve (AUC) of 0.781 (95% confidence interval (CI), 0.666 -0.897) in the discrimination between EOC and NEOC. The AUC of the combined model integrating clinical factors and radiomics features was 0.869 (95% CI, 0.783 -0.955). The nomogram demonstrated that the combined model provides a better net benefit to predict histological subtypes compared with radiomics signature and clinical factors alone when the threshold probability is within a range from 0.43 to 0.97.ConclusionsNomogram developed with CT radiomics signature and clinical factors is feasible to predict the histological subtypes preoperative for patients with OC.

【 授权许可】

Unknown